Presentation: Exploring Best Practices in Design of Experiments - JMP

More documents

Recommendations

Info

What if we are not willing to give up orthogonal main effects? • For 9 to 14 factors and 16 runs the alias optimal criterion can generate orthogonal designs with – No complete confounding of main effects and two-factor interactions. – We call these designs “no confounding” designs
Page 1 and 2: Exploring Best Practices in Design
Page 3 and 4: What Is a Designed Experiment? a st
Page 5 and 6: What Is a Response? A response is a
Page 7 and 8: What is a model? a simplified mathe
Page 9 and 10: ANOVA and regression models are equ
Page 11 and 12: The Model/Design Relationship - Par
Page 13 and 14: The Model/Design Relationship - Ali
Page 15 and 16: Design Optimality Criteria D-optima
Page 17 and 18: Module 1 - Conclusions 1. Remember
Page 19 and 20: Optimal Full Factorial Full Factor
Page 21 and 22: Properties of this design • Ortho
Page 23 and 24: Optimal Fractional Factorial Fract
Page 25 and 26: The 2 5-1 • Aliases: - All main e
Page 27 and 28: Module 2 - Summary 1. Main message
Page 29 and 30: Standard Fractional Factorial Desig
Page 31 and 32: Plackett-Burman Designs • These a
Page 33 and 34: JMP Demo
Page 35 and 36: If only we could find a design with
Page 37: How do we get generate this design?
Page 40 and 41: JMP Demo
Page 44 and 45: Recommended 9 Factor Design Absolut
Page 47 and 48: Definitive Screening Designs Engine
Page 49 and 50: Robust Screening Design Structure
Page 51 and 52: Definitive Screening Data Using Fiv
Page 53 and 54: Definitive Screening Correlation Ma
Page 55 and 56: Definitive Screening Main-Effects M
Page 57 and 58: Module 3 - Conclusions • The trad
Page 59: References Hall, M. Jr. (1961). Had

Presentation: Exploring Best Practices in Design of Experiments - JMP

Create successful ePaper yourself

Delete template?

Save as template?